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Fix regression for pandas < 2.0.0 in JSON loader #6978

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merged 1 commit into from
Jun 19, 2024
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A regression was introduced for pandas < 2.0.0 in PR:

As described in pandas docs, the dtype_backend parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html

This PR fixes the regression by passing (or not) the dtype_backend parameter depending on pandas version.

Maybe, in a future 3.0 datasets release, we could just require pandas > 2.0.

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@albertvillanova albertvillanova changed the title Fix regression for pandas < 2.0.0 Fix regression for pandas < 2.0.0 in JSON loader Jun 18, 2024
@HuggingFaceDocBuilderDev

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@albertvillanova albertvillanova merged commit e47a746 into main Jun 19, 2024
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@albertvillanova albertvillanova deleted the fix-6977 branch June 19, 2024 05:50
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PyArrow==8.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.005144 / 0.011353 (-0.006209) 0.003500 / 0.011008 (-0.007509) 0.063670 / 0.038508 (0.025162) 0.031793 / 0.023109 (0.008683) 0.239611 / 0.275898 (-0.036287) 0.276681 / 0.323480 (-0.046799) 0.004148 / 0.007986 (-0.003838) 0.002713 / 0.004328 (-0.001615) 0.048832 / 0.004250 (0.044582) 0.043066 / 0.037052 (0.006014) 0.256835 / 0.258489 (-0.001655) 0.292224 / 0.293841 (-0.001617) 0.027530 / 0.128546 (-0.101017) 0.010509 / 0.075646 (-0.065137) 0.203370 / 0.419271 (-0.215901) 0.035643 / 0.043533 (-0.007890) 0.252161 / 0.255139 (-0.002978) 0.271883 / 0.283200 (-0.011316) 0.018658 / 0.141683 (-0.123024) 1.081676 / 1.452155 (-0.370479) 1.142146 / 1.492716 (-0.350571)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.093484 / 0.018006 (0.075477) 0.298607 / 0.000490 (0.298117) 0.000220 / 0.000200 (0.000020) 0.000044 / 0.000054 (-0.000010)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.019021 / 0.037411 (-0.018390) 0.062471 / 0.014526 (0.047946) 0.075393 / 0.176557 (-0.101163) 0.121040 / 0.737135 (-0.616095) 0.077613 / 0.296338 (-0.218726)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.294857 / 0.215209 (0.079648) 2.931143 / 2.077655 (0.853489) 1.510866 / 1.504120 (0.006746) 1.379574 / 1.541195 (-0.161621) 1.352358 / 1.468490 (-0.116133) 0.561670 / 4.584777 (-4.023107) 2.378434 / 3.745712 (-1.367278) 2.713203 / 5.269862 (-2.556658) 1.706416 / 4.565676 (-2.859260) 0.062355 / 0.424275 (-0.361920) 0.004971 / 0.007607 (-0.002636) 0.336498 / 0.226044 (0.110453) 3.316464 / 2.268929 (1.047535) 1.833035 / 55.444624 (-53.611589) 1.532808 / 6.876477 (-5.343668) 1.537323 / 2.142072 (-0.604749) 0.639430 / 4.805227 (-4.165798) 0.115808 / 6.500664 (-6.384856) 0.043545 / 0.075469 (-0.031924)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 0.974428 / 1.841788 (-0.867360) 11.368914 / 8.074308 (3.294606) 9.754488 / 10.191392 (-0.436904) 0.146277 / 0.680424 (-0.534146) 0.013917 / 0.534201 (-0.520284) 0.286809 / 0.579283 (-0.292474) 0.267144 / 0.434364 (-0.167219) 0.326161 / 0.540337 (-0.214177) 0.418059 / 1.386936 (-0.968877)
PyArrow==latest
Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.005341 / 0.011353 (-0.006012) 0.003460 / 0.011008 (-0.007548) 0.050135 / 0.038508 (0.011627) 0.032014 / 0.023109 (0.008905) 0.259835 / 0.275898 (-0.016063) 0.286275 / 0.323480 (-0.037205) 0.004350 / 0.007986 (-0.003636) 0.002800 / 0.004328 (-0.001529) 0.049358 / 0.004250 (0.045107) 0.040182 / 0.037052 (0.003130) 0.278352 / 0.258489 (0.019863) 0.307869 / 0.293841 (0.014028) 0.029151 / 0.128546 (-0.099395) 0.010091 / 0.075646 (-0.065555) 0.058814 / 0.419271 (-0.360458) 0.033150 / 0.043533 (-0.010383) 0.263594 / 0.255139 (0.008455) 0.284065 / 0.283200 (0.000866) 0.017968 / 0.141683 (-0.123714) 1.145605 / 1.452155 (-0.306550) 1.196884 / 1.492716 (-0.295832)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.094045 / 0.018006 (0.076039) 0.299031 / 0.000490 (0.298541) 0.000210 / 0.000200 (0.000011) 0.000044 / 0.000054 (-0.000010)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.022510 / 0.037411 (-0.014901) 0.077478 / 0.014526 (0.062953) 0.087746 / 0.176557 (-0.088811) 0.129311 / 0.737135 (-0.607825) 0.089921 / 0.296338 (-0.206418)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.290279 / 0.215209 (0.075070) 2.880725 / 2.077655 (0.803070) 1.541262 / 1.504120 (0.037142) 1.424475 / 1.541195 (-0.116719) 1.436397 / 1.468490 (-0.032093) 0.578237 / 4.584777 (-4.006540) 0.965249 / 3.745712 (-2.780463) 2.682534 / 5.269862 (-2.587327) 1.732859 / 4.565676 (-2.832817) 0.065523 / 0.424275 (-0.358752) 0.005466 / 0.007607 (-0.002141) 0.343985 / 0.226044 (0.117940) 3.397463 / 2.268929 (1.128534) 1.929370 / 55.444624 (-53.515255) 1.605135 / 6.876477 (-5.271342) 1.753926 / 2.142072 (-0.388146) 0.659929 / 4.805227 (-4.145298) 0.118093 / 6.500664 (-6.382571) 0.041252 / 0.075469 (-0.034217)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.009177 / 1.841788 (-0.832610) 11.959624 / 8.074308 (3.885316) 10.484672 / 10.191392 (0.293280) 0.142085 / 0.680424 (-0.538339) 0.015955 / 0.534201 (-0.518245) 0.283649 / 0.579283 (-0.295634) 0.125681 / 0.434364 (-0.308683) 0.320490 / 0.540337 (-0.219847) 0.440353 / 1.386936 (-0.946583)

@albertvillanova
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Maybe a patch release will be needed with this fix.

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